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Organizational Productivity and Performance Measurements Using Predictive Modeling and Analytics

Organizational Productivity and Performance Measurements Using Predictive Modeling and Analytics

Copyright: © 2017 |Pages: 400
ISBN13: 9781522506546|ISBN10: 1522506543|EISBN13: 9781522506553
DOI: 10.4018/978-1-5225-0654-6
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MLA

Tavana, Madjid, et al., editors. Organizational Productivity and Performance Measurements Using Predictive Modeling and Analytics. IGI Global, 2017. https://doi.org/10.4018/978-1-5225-0654-6

APA

Tavana, M., Szabat, K., & Puranam, K. (Eds.). (2017). Organizational Productivity and Performance Measurements Using Predictive Modeling and Analytics. IGI Global. https://doi.org/10.4018/978-1-5225-0654-6

Chicago

Tavana, Madjid, Kathryn Szabat, and Kartikeya Puranam, eds. Organizational Productivity and Performance Measurements Using Predictive Modeling and Analytics. Hershey, PA: IGI Global, 2017. https://doi.org/10.4018/978-1-5225-0654-6

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Businesses are collecting massive amounts of data every day as a way to better understand their processes, competition, and the markets they serve. This data can be used to increase organizational productivity and performance; however, is essential that organizations collecting large data sets have the tools available to them to fully understand the data they are collecting.

Organizational Productivity and Performance Measurements Using Predictive Modeling and Analytics takes a critical look at methods for enhancing an organization’s operations and day-to-day activities through the effective use of data. Focusing on a variety of applications of predictive analytics within organizations of all types, this critical publication is an essential resource for business managers, data scientists, graduate-level students, and researchers.

Table of Contents

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Front Materials
Title Page
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Copyright Page
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Advances in Business Information Systems and Analytics (ABISA) Book Series
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Editorial Advisory Board and List of Reviewers
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Preface
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Chapters
Back Materials
Compilation of References
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About the Contributors
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Index
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